All methods were performed in accordance with the relevant guidelines and regulations. The study’s protocol was reviewed and approved by Partners Human Research Committee. The informed consent was obtained from all study participants and/or their legal guardians. The clinical data is derived from the electronic health records, which have been collecting patient data since 1990. The clinical data including patient family history, demographic information, diagnosis, medication records, lab test results and clinical notes. The Partners Biobank is a large integrated database which contains clinical data from Partners HealthCare for approximately 90,000 consented patients, and genomic data for approximately 25,000 of them. Further, we conducted a meta-analysis of our results combined with recent insomnia GWAS to discover novel genomic loci.Īll the clinical data and genetic data in this study were obtained from the Partners Biobank 22. In this study, we utilized a large-scale clinical database to explore the genetic underpinnings of insomnia and calculated the genetic correlation between insomnia and various clinical conditions. Electronic health records (EHRs) from large medical institutes comprise a uniquely valuable data source to help identify genetic associations within very specific clinical conditions 21. Because of complex underlying mechanisms of insomnia and its various clinical manifestations, obtaining a clinically well-defined subject cohort is critical for genetic association analysis. Since insomnia can be a chronic process with different trajectories and multiple complications in clinical settings, it is important to conduct studies specifically targeting clinical patient populations 20. In most previous studies, insomnia phenotypes were assessed through self-report, which could miss useful information and reflect only part of disorder status. Other studies have identified several insomnia related genes, such as CACNA1C 16, RBFOX3 17, PAX8 18 and MEIS1 19. These studies also identified genetic correlations between insomnia and various clinical conditions, such as schizophrenia, type 2 diabetes, and depression 13, 15. Another study using survey data of soldiers in the Army Study To Assess Risk and Resilience in Servicemembers (STARRS) study identified one significant locus 15. In two recent studies, large-scale cohorts were developed using data from UK Biobank and the combination of UK Biobank and 23andMe yielding 57 and 202 significant loci, respectively 13, 14. Candidate gene studies have highlighted genetic variants in numerous systems including the circadian gene CLOCK 9, the GABAergic system 10, the adenosinergic system 11, and the serotonergic system 12.Ī number of genome-wide association studies (GWAS) have been conducted examining the insomnia phenotype. Genetic contributions to insomnia have been demonstrated in both family and twin studies with the reported heritability being estimated at 25–45% 8. It is commonly comorbid with other physical and psychiatric disorders 6, 7.

It is characterized by heterogeneous phenotypes and equifinality, which might reflect different underlying causal mechanisms 4, including life style, stress and molecular mechanisms (for a review, see 5). Insomnia is a highly prevalent sleep disorder characterized by the inability to fall asleep or maintain sleep 1 and affects 10–20% of the adult population 2, 3.

The genetic correlation analysis yielded a strong positive genetic correlation between insomnia and alcohol use (rG = 0.56, se = 0.14, p < 0.001), nicotine use (rG = 0.50, se = 0.12, p < 0.001) and opioid use (rG = 0.43, se = 0.18, p = 0.02) disorders, suggesting a significant common genetic risk factors between insomnia and substance use. We further conducted a large-scale meta-analysis of our results and summary statistics of two recent insomnia GWAS and 13 significant loci were identified.

The heritability analysis indicated that common SNPs accounts for 7% (se = 0.02, p = 0.015) of phenotypic variation. Our genome-wide association study (GWAS) identified one novel genomic risk locus on chromosome 8 (lead SNP rs17052966, p = 4.53 × 10 −9, odds ratio = 1.28, se = 0.04). Diagnostic codes were used to identify 3,135 patients with insomnia. In this study, we combined electronic health record (EHR) derived phenotypes and genotype information to conduct a genome wide analysis of insomnia in a 18,055 patient cohort. It is further associated with substance usage and dependence, as well other psychiatric disorders. Insomnia is one of the most prevalent and burdensome mental disorders worldwide, affecting between 10–20% of adults and up to 48% of the geriatric population.

Icd 10 insomnia disorder